模糊粗糙集与模糊粗糙神经网络特征选择研究进展

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI:10.1002/widm.1402

Wanting Ji, Y. Pang, Xiaoyun Jia, Zhongwei Wang, Feng Hou, Baoyan Song, Mingzhe Liu, Ruili Wang

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引用次数: 29

摘要

特征选择是根据一定的评价标准从原始特征集中选择出一个特征子集。由于特征选择可以实现高效的特征约简，它已经成为许多数据挖掘任务中数据预处理的关键方法。由于在大多数情况下无法通过穷举搜索获得最优/简化的特征子集，因此近年来开发了许多特征选择策略。在这些策略中，模糊粗糙集理论已被证明是处理不确定信息的理想选择。本文综述了模糊粗糙集理论和两种基于模糊粗糙集理论的特征选择方法，即基于模糊粗糙集的特征选择方法和基于模糊粗糙神经网络的特征选择方法。本文综述了模糊粗糙理论及其在特征选择中的应用。此外，还讨论了两种特征选择方法存在的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Feature selection aims to select a feature subset from an original feature set based on a certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it has become a key method for data preprocessing in many data mining tasks. Recently, many feature selection strategies have been developed since in most cases it is infeasible to obtain an optimal/reduced feature subset by using exhaustive search. Among these strategies, fuzzy rough set theory has proved to be an ideal candidate for dealing with uncertain information. This article provides a comprehensive review on the fuzzy rough set theory and two fuzzy rough set theory based feature selection methods, that is, fuzzy rough set based feature selection methods and fuzzy rough neural network based feature selection methods. We review the publications related to the fuzzy rough theory and its applications in feature selection. In addition, the challenges in the two types of feature selection methods are also discussed.

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.